docs: def fit(self, values, bins):
          """
          Fit the transform using the given values (in our case ic50s).
  
          Parameters
          ----------
          values : ic50 values
          bins : bins for the cumulative distribution function
              Anything that can be passed to numpy.histogram's "bins" argument
              can be used here.
          """
          assert self.cdf is None
          assert self.bin_edges is None
          assert len(values) > 0
          (hist, self.bin_edges) = numpy.histogram(values, bins=bins)
          self.cdf = numpy.ones(len(hist) + 3) * numpy.nan
          self.cdf[0] = 0.0
          self.cdf[1] = 0.0
          self.cdf[-1] = 100.0
          numpy.cumsum(hist * 100.0 / numpy.sum(hist), out=self.cdf[2:-1])
          assert not numpy.isnan(self.cdf).any()